1. Technical Field
The present disclosure generally relates to predictive analytics for document content driven processes and more particularly to generating predictions from a probabilistic process model.
2. Discussion of Related Art
Semi-structured processes are emerging in industries such as government, insurance, banking and healthcare. These business or scientific processes depart from the traditional structured and sequential predefined processes. The lifecycle of semi-structured processes is not fully driven by a formal process model. While an informal description of the process may be available in the form of a process graph, flow chart or an abstract state diagram, the execution of a semi-structured process is not completely controlled by a central entity, such as a workflow engine. Case oriented processes are an example of semi-structured business processes.
Case executions within a process are typically non-deterministic, driven by human decision making, and the contents of documents. In particular there is no single formal process model that drives the lifecycle of case-oriented business processes. The lifecycle of semi-structured processes is not fully driven by a formal process model.
Assigning tasks to employees and other resources while minimizing the total penalties belongs to a class of NP-hard problems. Optimization for task scheduling is a well-studied problem. For example, task scheduling for people involved in Service Level Agreements (SLAs) has been studied in detail in the past. Known schedulers seek to minimize a sum of the exposed business impact, based on absolute or relative completion times. The scheduler assigns tasks to service personnel on the basis of a job-based task management model. These problems focus on scheduling tasks between multiple people while optimizing an objective function such as time to complete a task that is not influenced by the properties of the current business process instance.
According to an embodiment of the present disclosure, a need exists for a method of generating predictions from a probabilistic process model.